


import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
    from .tokenization_fnet import FNetTokenizer
else:
    __UpperCamelCase     :    Any 	=				None

__UpperCamelCase     :    List[str] 	=				logging.get_logger(__name__)
__UpperCamelCase     :    Any 	=				{'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

__UpperCamelCase     :    Union[str, Any] 	=				{
    'vocab_file': {
        'google/fnet-base': 'https://huggingface.co/google/fnet-base/resolve/main/spiece.model',
        'google/fnet-large': 'https://huggingface.co/google/fnet-large/resolve/main/spiece.model',
    },
    'tokenizer_file': {
        'google/fnet-base': 'https://huggingface.co/google/fnet-base/resolve/main/tokenizer.json',
        'google/fnet-large': 'https://huggingface.co/google/fnet-large/resolve/main/tokenizer.json',
    },
}

__UpperCamelCase     :    Optional[Any] 	=				{
    'google/fnet-base': 512,
    'google/fnet-large': 512,
}

__UpperCamelCase     :    str 	=				'▁'



class 				_UpperCamelCase			(    __lowerCAmelCase    ):

   '''simple docstring'''







   a_					:				Union[str, Any]     =			VOCAB_FILES_NAMES
   a_					:				int     =			PRETRAINED_VOCAB_FILES_MAP
   a_					:				str     =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   a_					:				Optional[Any]     =			["input_ids", "token_type_ids"]
   a_					:				Tuple     =			FNetTokenizer

   def __init__(	self	:			List[Any]							,	_lowerCamelCase	:			str=None							,	_lowerCamelCase	:			int=None							,	_lowerCamelCase	:			List[str]=False							,	_lowerCamelCase	:			Dict=True							,	_lowerCamelCase	:			str=True							,	_lowerCamelCase	:			Dict="<unk>"							,	_lowerCamelCase	:			Any="[SEP]"							,	_lowerCamelCase	:			Tuple="<pad>"							,	_lowerCamelCase	:			Optional[Any]="[CLS]"							,	_lowerCamelCase	:			Optional[Any]="[MASK]"							,	**_lowerCamelCase	:			List[Any]							,	):






         '''simple docstring'''



         __lowerCamelCase  :   List[Any]							    =    (
             AddedToken(lowerCamelCase_							,	lstrip=lowerCamelCase_							,	rstrip=lowerCamelCase_							,	normalized=lowerCamelCase_				)
             if isinstance(lowerCamelCase_							,	lowerCamelCase_				)
             else mask_token
         )

         super().__init__(
             lowerCamelCase_							,	tokenizer_file=lowerCamelCase_							,	do_lower_case=lowerCamelCase_							,	remove_space=lowerCamelCase_							,	keep_accents=lowerCamelCase_							,	unk_token=lowerCamelCase_							,	sep_token=lowerCamelCase_							,	pad_token=lowerCamelCase_							,	cls_token=lowerCamelCase_							,	mask_token=lowerCamelCase_							,	**lowerCamelCase_							,	)

         __lowerCamelCase  :   Tuple							    =    do_lower_case
         __lowerCamelCase  :   Any							    =    remove_space
         __lowerCamelCase  :   str							    =    keep_accents
         __lowerCamelCase  :   Optional[int]							    =    vocab_file
         __lowerCamelCase  :   Dict							    =    False if not self.vocab_file else True

   def 	_snake_case     (	self	:			Any							,	_lowerCamelCase	:			List[int]							,	_lowerCamelCase	:			Optional[List[int]] = None				):






         '''simple docstring'''



         __lowerCamelCase  :   str							    =    [self.sep_token_id]
         __lowerCamelCase  :   Dict							    =    [self.cls_token_id]
         if token_ids_a is None:
               return cls + token_ids_a + sep
         return cls + token_ids_a + sep + token_ids_a + sep

   def 	_snake_case     (	self	:			Union[str, Any]							,	_lowerCamelCase	:			List[int]							,	_lowerCamelCase	:			Optional[List[int]] = None				):






         '''simple docstring'''



         __lowerCamelCase  :   Dict							    =    [self.sep_token_id]
         __lowerCamelCase  :   Optional[Any]							    =    [self.cls_token_id]

         if token_ids_a is None:
               return len(cls + token_ids_a + sep				) * [0]
         return len(cls + token_ids_a + sep				) * [0] + len(token_ids_a + sep				) * [1]


   def 	_snake_case     (	self	:			str							,	_lowerCamelCase	:			str							,	_lowerCamelCase	:			Optional[str] = None				):






         '''simple docstring'''



         if not os.path.isdir(lowerCamelCase_				):
               logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""				)
               return
         __lowerCamelCase  :   Optional[int]							    =    os.path.join(
             lowerCamelCase_							,	(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]				)

         if os.path.abspath(self.vocab_file				) != os.path.abspath(lowerCamelCase_				):
               copyfile(self.vocab_file							,	lowerCamelCase_				)

         return (out_vocab_file,)




from datetime import datetime

import requests
from bsa import BeautifulSoup

if __name__ == "__main__":
     _SCREAMING_SNAKE_CASE 		=      input("""Enter image url: """).strip()
     print(F'''Downloading image from {url} ...''')
     _SCREAMING_SNAKE_CASE 		=      BeautifulSoup(requests.get(url).content, """html.parser""")
     # The image URL is in the content field of the first meta tag with property og:image
     _SCREAMING_SNAKE_CASE 		=      soup.find("""meta""", {"""property""": """og:image"""})["""content"""]
     _SCREAMING_SNAKE_CASE 		=      requests.get(image_url).content
     _SCREAMING_SNAKE_CASE 		=      F'''{datetime.now():%Y-%m-%d_%H:%M:%S}.jpg'''
     with open(file_name, """wb""") as fp:
          fp.write(image_data)
     print(F'''Done. Image saved to disk as {file_name}.''')
